Lane marking localization

ABSTRACT

Various embodiments of the present disclosure provide a system and method for lane marking localization that may be utilized by autonomous or semi-autonomous vehicles traveling within the lane. In the embodiment, the system comprises a locating device adapted to determine the vehicle&#39;s geographic location; a database; a region map; a response map; a camera; and a computer connected to the locating device, database, and camera, wherein the computer is adapted to: receive the region map, wherein the region map corresponds to a specified geographic location; generate the response map by receiving information form the camera, the information relating to the environment in which the vehicle is located; identifying lane markers observed by the camera; and plotting identified lane markers on the response map; compare the response map to the region map; and generate a predicted vehicle location based on the comparison of the response map and region map.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation of U.S. application Ser. No.15/896,077, entitled “LANE MARKING LOCALIZATION”, filed Feb. 14, 2018.The entire content of the above-mentioned patent application isincorporated by reference as part of the disclosure of this document.

BACKGROUND OF THE INVENTION Field of the Invention

The present disclosure relates to unmanned vehicle guidance and, morespecifically, a system for and method of sensing a roadway lane.

Description of Related Art

Global Positioning System (“GPS”) technology is widely used as a meansfor locating an automobile upon a roadway. As autonomous andsemi-autonomous vehicles become more advanced, accurately knowing thevehicle's position in the roadway becomes critical. For example,self-driving cars by Volvo and Tesla have been easily confused by fadedlane markers and other shabby road conditions. Further, current GPStechnology is inaccurate. To achieve a fully autonomous self-drivingvehicle requires the ability of a computer to determine the vehicle'slateral position within a roadway with great precision. Additionally,advanced driver-assistance systems (“ADAS”) benefit greatly from thisability. For example, lane keeping assistance (“LKA”) systems, lanedeparture warning (“LDW”) systems, and lane change assistance systemswould be greatly benefited by accurately knowing the vehicle's lateralposition within a lane. Other examples of ADAS systems include adaptivecruise control, adaptive light control, anti-lock braking systems,automatic parking, blind spot monitoring, collision avoidance systems,intersection detection, lane departure warning systems, parking sensors,turning assistance, and wrong-way driving warning.

A vehicle may utilize various levels of autonomous driving. For example,a first level of autonomous driving may assist a human driver duringsome driving tasks such as steering or engine acceleration/deceleration.A second level of autonomous driving may conduct some steering andacceleration/deceleration while the human driver monitors the drivingenvironment and controls the remaining driving tasks. Such a system isreferred to as a partially automated system. A third level of autonomousdriving may conduct driving tasks and monitor the driving environment,but the human driver must be ready to retake control when the automatedsystem requests. Such a system is generally referred to as aconditionally automated system. A fourth level of autonomous driving maydrive the vehicle and monitor road conditions; the human driver does notneed to take control but the system may only operate in specificconditions and environments such as inside of a factory, on a closedroad course, or within a bounded area. Such a system is referred to as ahighly automated system. A fifth level of autonomous driving may performall driving and road-monitoring tasks in all driving conditions. Such asystem is referred to as a fully-automated system.

Current technology relies on GPS technology to determine a vehicle'slateral position within a roadway. However, this method is susceptibleto a high amount of drift—the lateral area around the vehicle that iswithin the technology's margin of error. The amount of drift in a givensystem is dependent on many factors including signal strength and theprecision of the GPS hardware being used. Typical GPS devices aimed atthe average consumer have a drift of about IO meters. Even with the mostprecise instruments having the best signal strength, a systemexperiences a drift of I-2 meters or more, which is unacceptable forself-driving vehicles.

To improve the accuracy of GPS positioning, current technology alsoemploys an inertial measurement unit (“IMU”). An IMU is an electronicdevice that measures and reports a vehicle's specific force and angularrate using a combination of accelerometers and gyroscopes. However, evenwhile being augmented with IMU's, current lateral locating methods andsystems still experience a high amount of drift. For such a system to beuseful in a self-driving vehicle, the resolution needs to beapproximately IO cm or less.

Therefore, what is needed is a system that can utilize GPS informationand determine a vehicle's lateral position within a roadway with greataccuracy. This need has heretofore remained unsatisfied.

SUMMARY OF THE INVENTION

The present disclosure overcomes these and other deficiencies of theprior art by providing a method for determining a vehicle's locationcomprising the steps of approximating the vehicle's region, receiving aregion map from a database, wherein the region map corresponds to thevehicle's approximated region and comprises a plurality of region pointsindicating an expected roadway lane, receiving a response imagegenerated by an imaging device, the response image comprisinginformation relating to the vehicle's environment, generating a responsemap from the response image, the response map comprising a plurality ofresponse points indicating the vehicle's location, comparing theresponse map to the region map, and predicting the vehicle's locationbased on the differences between the response points and the regionpoints.

In another exemplary embodiment of the present disclosure, the vehicle'sregion may be approximated using a GPS device or an IMU device.

In another exemplary embodiment of the present disclosure, the step ofgenerating a response map further comprises the steps of detecting lanemarkers in the response image, the lane markers pertaining to physicalaspects contained in the response image, and plotting the responsepoints on the response map, the response points indicating locations ofthe lane markers.

In another exemplary embodiment of the present disclosure, the methodmay further comprise the step of generating a confidence score.

In another exemplary embodiment of the present disclosure, the responseimage may be generated from radar sensing equipment, LIDAR sensingequipment, GPS sensing information, and/or images.

In another exemplary embodiment of the present disclosure, the regionmap and the response map may be compared at a selected frequency.

In another exemplary embodiment of the present disclosure, the selectedfrequency may be at least 20 cycles per second.

In another exemplary embodiment of the present disclosure, the methodmay further comprise the step of outputting the vehicle's predictedlocation to an ADAS.

In another exemplary embodiment of the preset disclosure, the imagingdevices may comprise a plurality of imaging devices, each adapted toperceive different aspects of the vehicle's environment.

In overcoming the limitations currently available in the art, anotherexemplary embodiment provides a system for determining a vehicle'slocation on a roadway comprising a locating device adapted to determinea vehicle's geographic region, a database comprising a plurality ofregion maps, the region maps comprising a plurality of region points, animaging device adapted to perceive information relating to the vehicle'senvironment, a processor operably connected to the locating device, thedatabase, and the imaging device, the processor, at a predeterminedfrequency, adapted to receive, from the locating device, the vehicle'sdetermined geographic region, receive, from the database, the region mapcorresponding to the vehicle's determined geographic region, receive,from the imaging device, information perceived relating to the vehicle'senvironment, generate a response map, the response map comprising aplurality of response points corresponding to lane markers detectedwithin the response map, compare the response map to the region map, anddetermine the vehicle's predicted location based on the comparison ofthe region map and response map.

In another exemplary embodiment of the present disclosure, the locatingdevice may comprise a GPS device or an IMU device.

In another exemplary embodiment of the present disclosure, the imagingdevice may comprise a camera or a LIDAR device.

In another exemplary embodiment of the present disclosure, thepredetermined frequency is at least 20 cycles per second.

In another exemplary embodiment of the present invention, the processormay be further configured to output the vehicle's predicted location toan ADAS.

In another exemplary embodiment of the present disclosure, the processormay be further configured to determine a confidence score.

In another exemplary embodiment of the present disclosure, the systemmay further comprise a plurality of imaging devices, each adapted toperceive different aspects of the vehicle's environment.

In some embodiments, the present disclosure provides various vehiclesystems the ability to recognize and track lane markings, which, inturn, may be used to allow on-board monitors to detect and/or correctthe vehicle's location. For example, the present disclosure may interactwith a lane keeping assistance (“LKA”) system, lane departure warning(“LDW”) system, and lane change assistance system. Such examples mayutilize the present disclosure because such systems need to know therelative position the vehicles within the ego-lane and/or the lane tothe left/right.

The foregoing, and other features and advantages of the invention, willbe apparent from the following, more particular description of thepreferred embodiments of the invention, the accompanying drawings, andthe claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present disclosure, the objectsand advantages thereof, reference is now made to the ensuingdescriptions taken in connection with the accompanying drawings brieflydescribed as follows:

FIG. 1 illustrates a system for determining a vehicle's position withina lane, according to an exemplary embodiment of the present disclosure;

FIG. 2 illustrates a method for determining a vehicle's position withina lane, according to an exemplary embodiment of the present disclosure;

FIG. 3A illustrates an image taken by the camera, according to anexemplary embodiment of the present disclosure;

FIG. 3B illustrates a response map, according to an exemplary embodimentof the present disclosure;

FIG. 3C illustrates a lane map, according to an exemplary embodiment ofthe present disclosure; and

FIG. 4 illustrates updated vehicle location, according to an exemplaryembodiment of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS

Further features and advantages of the disclosure, as well as thestructure and operation of various embodiments of the disclosure, aredescribed in detail below with reference to the accompanying FIGS. 1-4 .Although the disclosure is described in the context of a genericvehicle, the term vehicle refers to any type of motorized groundtransport including, but not limited to, cars, trucks, carts, sleds,lifts, and rovers.

In an exemplary embodiment of the present disclosure, the systemutilizes hardware including a camera, a database, and a computer, toiteratively update the vehicle's predicted location and to determine thevehicle's position relative to the lane of traffic in which it istraveling. In one embodiment, the camera is installed on the vehicle andits position and view angle are predetermined relative to the rest ofthe vehicle on which it is installed. For example, the camera may beinstalled on the roof of the vehicle at the centerline of the vehicle,and pointed in the direction of travel, i.e., forward, such that thecamera is out of the view of the driver. The computer is configured toinclude the camera's position and orientation relative to the vehicle.The computer fetches data from the camera and generates a response map.The response map is generated by identifying and locating laneindicators depicted the camera's data. The computer fetches data fromthe database, including a region map. The region map comprisesinformation previously gathered by a collection vehicle equipped withlane sensing devices including radar, LIDAR, GPS, and cameras. In suchan embodiment, the collection vehicle, along with the equipment thereon,accurately determines the collection vehicle's location in relation tolane markers. Such lane markers include traditional lane markings suchas lines painted in a roadway and reflectors. Lane markers may alsoinclude permanent or semi-permanent structures such as raised curbs,barricades, retaining walls, roadway shoulders, roadway barriers,bridges, buildings, street signs, tunnels, trees, any support structuresthereof.

The present disclosure utilizes the collection vehicle's accuratelydetermined roadway position relative to lane markers as a baseline todetermine a subsequent vehicle's location within the same roadway at asimilar longitudinal roadway position. For example, the subsequentvehicle may be referred to as the “target vehicle.” As the targetvehicle moves down a roadway, it captures information to generate theresponse map. The response map comprises information similar to that ofa region map. The target vehicle may have a lateral position within theroadway different from that of the collection vehicle's lateral positionat the same longitudinal roadway position. The present disclosuredetermines the target vehicle's location within the roadway by comparingthe response map—the location information captured by the targetvehicle—against the region map—the location information captured bycollection vehicle. In doing so, the target vehicle's lateral roadwayposition is accurately determined relative to (1) the lane indicatorsperceived by target vehicle and (2) the collection vehicle's accuratelydetermined roadway position relative to the same lane indicators.

The computer compares the response map against the region map todetermine a confidence score. The confidence score is a numericalrepresentation of the differences between the response map and theregion map; the greater the difference, the lower the confidence score.If the confidence score is below a predetermined threshold, the computergenerates the vehicle's predicted position. The predicted position isgenerated by comparing the region map against the response map anddetermining the vehicle's likely position based on the relativeperspectives of the response map and the region map. In anotherembodiment, the computer generates the vehicle's predicted position atany confidence score. The system may output the updated locationinformation to another system. For example, the system may output theupdated location information to the vehicle's self-driving automationsystem and/or an ADAS system installed on the vehicle. In anotherexample, the system may output the updated location information to adatabase or another vehicle. Such a system may be implemented as part ofan automated self-driving system that steers the vehicle in response tothe updated location information. In another embodiment, such a systemmay be implemented as part of an ADAS.

In an exemplary embodiment of the present disclosure and with referenceto FIG. 1 , a system 100 is utilized as part of a vehicle to determinethe vehicle's location within a lane. The system may comprise a GPSdevice 120 to provide an approximation of the vehicle's actual locationand an IMU 110 to estimate the vehicle's speed and velocity. The systemmay also comprise a database 140 comprising environment data 130. Theenvironment data 130 may comprise a plurality of region maps. The systemmay also comprise a camera 150 configured to perceive informationrelating to the vehicle's environment. The system may also comprise acomputer 160 and an output device 170.

In an embodiment, the GPS device 120 gathers information comprising thelatitude, longitude, and/or the position of the vehicle. In anotherembodiment, the GPS information comprises the vehicle's yaw angle and/ordirection of travel. In another embodiment, the GPS informationcomprises a time and date stamp. The GPS device 120 may receiveinformation from orbiting satellites that are widely used in the currentstate of the art. General GPS signaling and positioning is readilyapparent to one skilled in the art.

The database 140 comprises environment data 130 obtained using GPSand/or radar, and/or light detection and ranging (“LIDAR”), and/orphotographic cameras, and/or video graphic cameras. In such anembodiment, a collection vehicle (not shown) comprises equipmentincluding radar, LIDAR, GPS, and cameras. The equipment installed on thecollection vehicle accurately gathers location information as it isdriven down a roadway. This location information is uploaded to thedatabase 140 and is used to create the environment data 130. Theenvironment data 130 is used to identify physical structures that aidthe system in determining a vehicle's position relative to thosestructures. For example, the environment data 130 may comprise locationinformation relating to lane markers including lane separators, lanemarkings, and reflectors. In such an embodiment, the collection vehicleaccurately determines its physical location relative to the lanemarkers. In another embodiment, the environment data 130 furthercomprises data relating to permanent or semi-permanent structuresincluding bridges, signs, buildings, barriers, street lights, raisedcurbs, trees, support structures thereof, and other physical structures.In one embodiment, the database 140 is located remotely. In anotherembodiment, the database 140 is located on the vehicle. In anotherembodiment, the database 140 may comprise a plurality of local or remotedatabases, communicatively connected to one-another and to the computer160.

The camera 150 may be installed on the vehicle having any orientation orview angle. For example, the camera 150 may be installed such that itpoints in the direction of travel, i.e., towards the front of thevehicle. In another embodiment, the camera 150 may be installed suchthat it points in a direction other than the direction of travel, i.e.,towards the rear or sides of the vehicle. In one embodiment, the camera150 comprises a video camera gathering video at a predetermined rate. Inanother embodiment, the camera 150 comprises a video camera with framerate of at least 20 frames per second. In another embodiment, the camera150 comprises a photographic camera capturing images at a predeterminedrate. In an embodiment, the camera 150 comprises a photographic cameracapturing images at rate of at least 10 frames per second. In anotherembodiment, the camera 150 may be configured to capture informationbeyond that visible to the human eye. For example, the camera 150 may beconfigured to capture infrared light and/or ultraviolet light. While acamera is disclosed herein, the disclosed subject matter is not limitedto a camera comprising a lens and/or a light sensor or film. Forexample, the camera 150 may be a depth sensor. In such an embodiment,the camera 150 comprises a light-emitting device and a sensor capable ofdetecting the light emitted from that device. In such an embodiment,light-emitting device emits a plurality of beams of light, for example,infrared laser beams. The plurality of infrared lasers reflect light offof various surfaces and structures, for example, roadway reflectors andlane markers. The camera's 150 sensor detects the infrared lasers andgenerates a depth map of the environment perceived by the camera 150.Further, as disclosed here, the camera 150 may comprise a plurality ofcameras pointed in the same or differing directions. In such anembodiment, the plurality of cameras may be installed at differentlocations on the vehicle.

The present disclosure includes a computer 160 for processing the datafrom the GPS 120, the database 140, and the camera 150. The computer 160generates the vehicle's predicted location by comparing the vehicle'sapproximate location fetched from the GPS 120, the environment data 130fetched from the database 140, and the information gathered from thecamera 150. The environment data may include a region map (not shown).In one embodiment, the system uses the GPS 120 to determine anapproximate location of the vehicle. The computer 160 fetchesenvironment data 130 relating to the vehicle's approximate location, asdetermined by the GPS 120. The computer 160 fetches data from the camera150. The computer 160 determines the vehicle's predicted location bycomparing the environment data 130 against the data fetched from thecamera 150.

In another embodiment, the computer 160 also determines a confidencescore that correlates to how well the data fetched from the camera 150matches the environment data 130 fetched from the GPS 120 and thedatabase 140.

After the computer 160 determines the vehicle's predicted location, thecomputer 160 may output that information to an output device 170. Forexample, the computer 160 may output the vehicle's predicted location toself-driving automation system. In another embodiment, the computer 160may output the vehicle's predicted location to an ADAS. In anotherembodiment, the computer 160 may output the vehicle's predicted locationto a database.

FIG. 2 illustrates a method 200 for determining a vehicle's positionwithin a lane, according to an exemplary embodiment of the disclosure.At step 210, a computer fetches GPS and/or IMU location information froman GPS device and/or an IMU device. At step 220, the computer generatesa region map comprising previously-gathered information relating to theenvironment in which the vehicle is traveling. For example, the regionmap comprises information previously gathered by a collection vehicleusing radar, LIDAR, GPS, and/or cameras. Such information pertained tothe collection vehicle's location on a specific roadway relative toother roadways in the area, lane-specific information relative to thelane in which the collection vehicle is traveling, and informationrelating to the collection vehicle's speed, direction of travel, and/orvelocity relative to the location information. In one embodiment, thecomputer generates the region map. In another embodiment, the computerreceives the region map from a database.

At step 230, the system utilizes a camera installed on the vehicle. Inone embodiment, the camera is installed on the vehicle having apredetermined viewing angle and orientation. For example, the camera isinstalled on the roof of the vehicle, centered on the vehicle'scenterline, and pointing in the direction of travel, i.e., forward. Thecamera captures an image of the region in front of the vehicle. Inanother embodiment, the camera may capture video and/or photographicimages at a predetermined frame rate. In another embodiment, the cameracaptures infrared and/or ultraviolet light. In one embodiment, thecamera captures images at a predetermined rate. In another example, thecamera captures images at a rate of at least 10 images per second.

At step 240, the system generates a response map based on informationfetched from the camera. The response map may be generated in real-timeor in near real-time. The response map may be generated on apredetermined interval, for example, 20 times per second. In oneembodiment, the system uses an image fetched from the camera andidentifies lane markers within the lanes of vehicle travel depicted inthe image. The camera may identify other aspects of the roadwayincluding, but not limited to, bridges, signs, barriers, street lights,and buildings. In one embodiment, the computer comprisescomputer-executable code configured to detect permanent and/orsemi-permanent structures within a two-dimensional image. In such anembodiment, the computer analyzes the image captured from the camera andidentifies lane indicators such as painted lines and reflectors. Thecomputer may also identify other structures such as bridges, signs,barriers, street lights, and buildings. The computer may generate aresponse map on a predetermined interval. In one embodiment, thecomputer generates a response map at least ten times per second.

At step 250, the system generates the vehicle's predicted location andcalculates a confidence score for determining the vehicle's lateralposition within a lane. For example, the system determines the predictedlocation by comparing the region map against the response map. In suchan embodiment, the system samples various points within the region mapidentifying lanes of vehicle travel. The system samples the response mapand identifies lanes of travel depicted therein. The system thencompares this sampled region map to the response map and generates thevehicle's predicted location based on the differences in theperspectives of the region and response maps. In such an embodiment, thesystem takes the GPS/IMU information, the region map, and the responsemap as arguments in calculating the vehicle's predicted location. Forexample, if the region map is substantially the same as the response mapbut skewed to the left, the system's comparison recognizes the vehicle'sactual position must be to the right of the GPS location. The systemgenerates a predicted vehicle location based those differences.

In another embodiment, at step 250, the system calculates a confidencescore. In one embodiment, for example, where the region map and theresponse map are identical, the system generates a confidence score of1.000. In such an example, the environment data was gathered using acollection vehicle that was located at the same physical location withthe same orientation of that of the system's vehicle. The confidencescore reflects the system's confidence in the vehicle's predictedposition compared to its position according to the region map, relativeto the vehicle's lateral position within a lane. For example, a score of1.000 correlates to a confidence of 100% and a score of 0.000 correlatesto a confidence of 0%. At step 260, the system outputs a predictedlocation. In one embodiment, the system may output the predictedlocation to an automated self-driving system. In another embodiment, thesystem may output the predicted location to an ADAS. In anotherembodiment, the system may output a corrected location if the confidencescore is below a predetermined threshold. For example, the scorethreshold is set at 0.900. If the system generates a confidence score ofanything less than 0.900, for example, a score of 0.85, the systemgenerates a corrected location based on the comparison of the sampledregion map and the response map. In an embodiment, the mathematicalvariance may be used as a confidence score. Further, if the systemgenerates a confidence score of, for example, 0.950, the system outputsthe vehicle's position as determined by the GPS/IMU information. Inanother embodiment, the system outputs the corrected location to an ADASand/or an automated self-driving system. In another embodiment, themathematical variance is used as the confidence score.

FIG. 3A illustrates an image taken by a single camera 120. Here, thecamera 120 is facing the direction of travel. In other embodiments ofthe disclosure, the camera is positioned such that the view captured isnot the direction of travel, e.g., facing behind the vehicle or toeither side. In other embodiments of the disclosure, a plurality ofcameras may be used. As disclosed herein, the camera may be installedanywhere on the vehicle having any orientation that allows the camera toview the vehicle's environment. When the camera is installed on thevehicle, the system may be updated as to the camera's positioningrelative to the rest of the vehicle and the direction of travel. Thesystem 100 analyzes the image taken by the camera and creates a responsemap by detecting lane markers such as sold lines 301, striped lines 303,and reflectors 302. In one embodiment, the camera may be permanentlyinstalled on the vehicle. For example, the camera may be integrated intothe vehicle's rearview mirror or a bumper. In another embodiment, thecamera may be temporarily installed on the vehicle. In anotherembodiment, the camera utilized may be included in a mobile device suchas a cell phone or tablet. In such an embodiment, the mobile device maybe temporarily installed on the vehicle and easily removed by a user.

FIG. 3B illustrates an exemplary response map according to an embodimentof the disclosure. The response map reflects lane markings as recognizedby the system. For example, the response map is a binary map indicatinglane markings 311 shown as black lines. In such an embodiment, thesystem analyzes the location information for lane indicators. When thesystem identifies a lane indicator, it plots a point on the response map(depicted as a black dot against a white background). Everything otherthan the relevant lane markings 311 are shown as white space 312. Thesystem plots a plurality of lane indicators on the response map,culminating as lane markers 311. The system may also use physicalstructures such as bridges, barriers, signs, and buildings to determinelane markings.

In one embodiment, the computer 160 comprises computer-executable,non-transient code configured to detect certain elements with an image.For example, the computer 160 recognizes lane markings within a roadwayincluding painted solid lines 301, painted striped lines 303, andreflectors 302. The system generates the response map as a seriespoints, culminating a lane marking lines 311. The response maprepresents the road ahead of the vehicle, viewed from the camera 150 andperceived by the computer 160. In other embodiments, the lane markings311 reflect other structural components such as bridges, signs, andbarriers (not shown).

FIG. 3C illustrates a lane map, according to an embodiment of thepresent disclosure. The lane map comprises a comparison of the regionmap against the response map. The region map is compiled using images,GPS, radar, and/or LIDAR information. The system plots plurality ofregion map points 322 (shown as circles). The region map points 322reflect lane markings as detected in the environment data 130. Theresponse map comprises a plurality lines 321 (shown as grey lines)indicating the lane markers as viewed from the camera and perceived bythe computer. The computer analyzes the differences between the regionmap and the response map and generates the vehicle's predicted location.

In another embodiment, the system determines a confidence score based onthe differences in the lane map, which is shown in the top-left corner.For example, a perfect match overlays with 100% accuracy, resulting in ascore of 1.000 (not shown). In another example, the system may determinea score of 0.74 where the overlay is a 74% match (as shown). In such anembodiment, the overlay is close, but the region map points 322 differsfrom the points from the response map lines 321 at some, but not all ofthe region map points 322. In such an embodiment, the score thresholdmay be 0.90, and in such an instance, the system would output apredicted vehicle location by analyzing the differences in the lane map.In another embodiment, the system may also determine other statisticalparameters, such as the variance. In such an embodiment, the variance iscalculated, for example, of 0.384 (as shown). For example, a logisticfunction may be used to calculate the variance, such as:

${variance} = \frac{x_{\min} + \left( {x_{\max} - x_{\min}} \right)}{1 + e^{S({{Gx} - m})}}$

where,

x_(min)=the minimum value

x_(max)=, the maximum value

S=the steepness

G=the growth rate

x=the matching score of the response map

m=the midpoint

FIG. 4 illustrates an updated vehicle location, according to anembodiment of the present disclosure. The predicted location 401 isdetermined using GPS and/or IMU locating information. The GPS location402 represents where the GPS perceives the vehicle to be relative to thelanes of travel. Notably, the GPS location 402 often varies greatly fromthe vehicle's actual location 403. Where the score is below apredetermined threshold, the system determines the predicted location401 and outputs that location. In such an embodiment, the systemrecognizes which lane of traffic the vehicle is traveling. In otherembodiments, the system determines the vehicle's predicted location 401regardless of the confidence score. In other embodiments, the system mayuse the confidence score to determine the vehicle's predicted location401.

Although a lane marking localization system has been shown anddescribed, lane marking localization systems may be implementedaccording to other embodiments of the disclosure. For example, thesystem may utilize a plurality of cameras or other information gatheringdevices such as radar or LIDAR. Other embodiments of the disclosure mayutilize a plurality of external or internal databases, on which relevantinformation is stored. Other embodiments also include those that outputinformation to vehicle driving aids such as navigation and ADAS systems.

In an embodiment of the disclosure, the methodologies and techniquesdescribed herein are implemented on a special purpose computerprogrammed to determine lane marking and relative vehicle position. Inan embodiment of the disclosure, the special-purpose computer comprisesan embedded system with a dedicated processor equipped as part of avehicle. In other embodiments, some or all of the components of thepresent disclosure may be integrated as part of a mobile device, forexample, a cell phone or a tablet. The disclosure has been describedherein using specific embodiments for the purposes of illustration only.It will be readily apparent to one of ordinary skill in the art,however, that the principles of the disclosure can be embodied in otherways. Therefore, the disclosure should not be regarded as being limitedin scope to the specific embodiments disclosed herein, but instead asbeing fully commensurate in scope with the following claims.

We claim:
 1. A method implemented by a processor, comprising:approximating a vehicle's region; receiving a region map from adatabase, wherein the region map corresponds to the vehicle'sapproximated region and comprises a plurality of region pointsindicating an expected roadway lane; receiving a response imagegenerated by an imaging device of one or more imaging devices of thevehicle, the response image comprising information relating to thevehicle's environment; generating a response map from the responseimage, the response map comprising a plurality of response pointsindicating the vehicle's location; comparing the response map to theregion map to determine (a) differences between the plurality ofresponse points and the plurality of region points, and (b) a confidencescore that comprises a variance that is computed using a logisticfunction; and predicting the vehicle's roadway position based on theconfidence score and the differences between the plurality of responsepoints and the plurality of region points.
 2. The method of claim 1,wherein the vehicle's region is approximated using a Global PositioningSystem (GPS) device or an inertial measurement unit (IMU) device.
 3. Themethod of claim 1, wherein the generating the response map furthercomprises: detecting lane markers in the response image, the lanemarkers pertaining to physical aspects contained in the response image;and plotting the response points on the response map, the responsepoints indicating locations of the lane markers.
 4. The method of claim1, further comprising: generating, based on the differences, theconfidence score.
 5. The method of claim 1, wherein the response imageis generated from radar sensing equipment, light detection and ranging(LIDAR) sensing equipment, Global Positioning System (GPS) sensinginformation, and/or images.
 6. The method of claim 1, wherein the regionmap and the response map are compared at a selected frequency.
 7. Themethod of claim 6, wherein the selected frequency is at least 20 cyclesper second.
 8. An apparatus, comprising: a processor configured to:approximate a vehicle's region; receive a region map from a database,wherein the region map corresponds to the vehicle's approximated regionand comprises a plurality of region points indicating an expectedroadway lane; receive a response image generated by an imaging device ofone or more imaging devices of the vehicle, the response imagecomprising information relating to the vehicle's environment; generate aresponse map from the response image, the response map comprising aplurality of response points indicating the vehicle's location; comparethe response map to the region map to determine (a) differences betweenthe plurality of response points and the plurality of region points, and(b) a confidence score that comprises a variance that is computed usinga logistic function; and predict the vehicle's roadway position based onthe confidence score and the differences between the plurality ofresponse points and the plurality of region points.
 9. The apparatus ofclaim 8, wherein the processor is further configured to: output thevehicle's predicted location to an advanced driver-assistance system(ADAS).
 10. The apparatus of claim 8, wherein each of the one or moreimaging devices is adapted to perceive a different aspect of thevehicle's environment.
 11. The apparatus of claim 8, wherein thevariance is computed as:${{variance} = \frac{x_{\min} + \left( {x_{\max} - x_{\min}} \right)}{1 + e^{S({{Gx} - m})}}},$wherein x is a matching score of the response map, x_(min) is a minimumvalue of the response map, x_(max) is a maximum value of the responsemap, S is a steepness parameter, G is a growth rate parameter, and m isa midpoint of the response map.
 12. The apparatus of claim 8, whereinthe imaging device comprises a light detection and ranging (LIDAR)device.
 13. The apparatus of claim 8, wherein the imaging devicecomprises a camera that captures images at a rate of at least 10 imagesper second.
 14. A non-transitory computer-readable storage medium havinginstructions stored thereupon, the storage medium comprising:instructions for approximating a vehicle's region; instructions forreceiving a region map from a database, wherein the region mapcorresponds to the vehicle's approximated region and comprises aplurality of region points indicating an expected roadway lane;instructions for receiving a response image generated by an imagingdevice of one or more imaging devices of the vehicle, the response imagecomprising information relating to the vehicle's environment;instructions for generating a response map from the response image, theresponse map comprising a plurality of response points indicating thevehicle's location; instructions for comparing the response map to theregion map to determine (a) differences between the plurality ofresponse points and the plurality of region points, and (b) a confidencescore that comprises a variance that is computed using a logisticfunction; and instructions for predicting the vehicle's roadway positionbased on the confidence score and the differences between the pluralityof response points and the plurality of region points.
 15. The storagemedium of claim 14, further comprising: instructions for comparing theconfidence score to a threshold; and instructions for outputting thevehicle's predicted roadway position based on the confidence score beinglower than the threshold.
 16. The storage medium of claim 14, whereinthe response map is generated using a camera pointing in a directionopposite to a direction of travel of the vehicle.
 17. The storage mediumof claim 14, wherein lane markers in the response map are based on oneor more physical structures.
 18. The storage medium of claim 17, whereinthe one or more physical structures comprise a bridge, a barrier, asign, or a building.
 19. The storage medium of claim 17, wherein thelane markers comprise at least one of solid lines, striped lines, orreflectors.
 20. The storage medium of claim 14, wherein the region mapfurther comprises information related to a speed of the vehicle, adirection of travel of the vehicle, or a velocity of the vehiclerelative to the expected roadway lane.